king county housing

First visualization

The following visualization is trying to visualize the relationship between housing price and the size of the living space. Since the original data is too big, the data are sorted based on zipcode.

Overall, there is a positive relationship between the average price and the average sqft of the living space.
Although this may vary based on the zipcode, most of the houses in King county have less than 3000 sqft living space and the accordingly price remain below one million dollar.

Second visualization

The following 2 graphs are indicating the number of sold houses in each month. Since the dataset only documents the data from beginning of May 2014 to ending of May 2015, we figure that make them seperate plots would be better for visualizations and comparisions. The reason why we use bar graph is that : 1. It indicates the number and the rank. 2. It’s discrete data.

From the data that we have, it’s obvious that the average number of sold houses in 2014 are significantly higher than that in 2015.
By comparing these two plots, we may conclude that people’s desire of purchasing houses vary along the temperature. During summer and spring, the sold numbers are relatively higher than the other 2 seasons.

Third visualization

The attached visualization is illustrating the correlation between the built year for the house and the average selling price within that year. The discrete data is arranged in chronological order. Since the data fluctuates a lot, a trendline would be considered as necessary to specify and indicate the overal trends.

Generally speaking, the correlation between average selling price and the built year of the house is obvious. Around 1955, there is a turning point for the correlation. Before 1955, there is a negative relationship between them. After 1955, they seem to be proportional to each other.
Most of the houses selling prices are within the range of 450 thousand and 700 thousand dollars.